1. Field of the Invention
The embodiments described herein relate generally to inspection of containers and, more particularly, to reconstructing and segmenting images of the contents of the containers to facilitate detecting objects concealed within the container.
2. Description of Prior/Related Art
At least some known detection systems construct an image of a container and analyze the image to detect explosives, drugs, weapons, and/or other contraband objects within the container. At least one known method for detecting objects within a container includes a first step of scanning the container with X-rays and reconstructing an image of the container, and a second step of inspecting the reconstructed image to detect objects of interest.
The first step involves reconstruction of an image, such as a computed tomography (CT) image, of the interior of the container. Each pixel in the reconstructed image generally represents a relative X-ray attenuation caused by the contents of the container corresponding to that pixel location. Known methods of image reconstruction include, for example, filtered backprojection (FBP) and iterative reconstruction (IR). Known implementations of these methods attempt to optimize performance based on various image-quality metrics. Such known image reconstruction algorithms typically have been developed for use by the medical community and, therefore, implementations of these algorithms are optimized for human interpretation of the images for the purpose of diagnosing disease.
At least some known detection systems accept such final reconstructed images as an input and apply various inspection algorithms to identify objects of interest within the image. Such inspection algorithms typically begin with a procedure, often referred to as image “segmentation” and/or “labeling,” to group the elements of the image data into clusters of like values that may correspond to particular objects or sub-objects within the container. Such known image segmentation procedures typically assume that certain inhomogeneities in the image data received from the reconstruction algorithm are artifacts induced in the reconstructed image through inconsistencies, noise, or errors in the measured X-ray data. As a result, detection systems using known image reconstruction and inspection algorithms must apply complex heuristic corrections during the inspection step to compensate for errors passed through from the image reconstruction step. The separation of image reconstruction and image segmentation into two independent steps thus decreases an accuracy of, and increases a time and cost required for inspection by, known detection systems.